Ai Video Editor App Free

Turn a 2-minute smartphone recording into 1080p edited MP4 clips just by typing what you need. Whether it's quickly editing raw footage into a shareable vide...

Security Scan

The skill appears to do what it says (upload user videos to a cloud service for editing) but contains metadata/instruction mismatches and asks to generate/use a bearer token and read install/config paths — these inconsistencies merit caution before installing.

Purpose & Capability

Skill description (cloud AI video editing) matches the API endpoints and upload/export flows in SKILL.md. Requesting a NEMO_TOKEN is coherent for a proprietary cloud service. However, registry metadata at top declared no required config paths while the skill frontmatter lists ~/.config/nemovideo/ as a configPath; also requires.env lists NEMO_TOKEN as required but SKILL.md provides an anonymous-token fallback flow if the env var is missing. These mismatches make the declared requirements unreliable.

Instruction Scope

Runtime instructions direct the agent to upload user video files and metadata to https://mega-api-prod.nemovideo.ai, create sessions, send SSE messages, and poll render status — all expected for a cloud video editor. The SKILL.md also instructs generating an anonymous token via a POST call if NEMO_TOKEN is absent, and to detect install path (e.g., ~/.clawhub/, ~/.cursor/skills/) to set X-Skill-Platform header — which implies the agent will attempt to inspect filesystem/install paths. That filesystem detection is broader than strictly needed for editing and is not declared consistently in the metadata.

Install Mechanism

Instruction-only skill with no install spec and no code files — nothing will be downloaded or written as part of an installer. This minimizes install-time execution risk.

Credentials

The only declared credential is NEMO_TOKEN which is appropriate for the described backend. But two issues: (1) SKILL.md will create and use an anonymous token if NEMO_TOKEN is absent (contradiction with 'required env var'), and (2) frontmatter claims a config path (~/.config/nemovideo/) even though the registry summary listed none. Both suggest the skill may access or expect local config/token storage beyond the single declared env var. You should not place high-privilege credentials in NEMO_TOKEN without confirming what permissions that token grants and where it's stored/used.

Persistence & Privilege

always is false (default) and the skill does not request persistent installation or modify other skills. Autonomous invocation is allowed (platform default) but not combined with other high privileges here.

What to consider before installing

This skill appears to be a straightforward cloud video-editing integration, but there are a few red flags you should consider before installing or using it: 1) The skill will upload any video you provide to https://mega-api-prod.nemovideo.ai — do not send sensitive or private footage unless you trust that service and its retention/privacy policy. 2) It lists NEMO_TOKEN as the required credential but also describes an anonymous-token creation flow; clarify whether the env var is actually required and what permissions a token has. 3) The frontmatter mentions a local config path (~/.config/nemovideo/) and instructs detecting install paths — ask the publisher why the skill needs to inspect local filesystem paths and whether it will read or write tokens/config files. 4) There is no homepage or publisher info in the registry entry; try to obtain a canonical homepage, privacy policy, or publisher contact before use. 5) Prefer using the anonymous token flow or a limited-scope token rather than placing high-privilege credentials in environment variables. If you need to proceed, test with non-sensitive sample videos and request explicit details from the publisher about token lifecycle, data retention, and where (if anywhere) tokens or uploads are stored locally.

Getting Started

Share your raw video clips and I'll get started on AI video editing. Or just tell me what you're thinking.

Try saying:

• "edit my raw video clips"
• "export 1080p MP4"
• "trim the pauses, add transitions, and"

Quick Start Setup

This skill connects to a cloud processing backend. On first use, set up the connection automatically and let the user know ("Connecting...").

Token check: Look for NEMO_TOKEN in the environment. If found, skip to session creation. Otherwise:

• Generate a UUID as client identifier
• POST https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id header
• Extract data.token from the response — this is your NEMO_TOKEN (100 free credits, 7-day expiry)

Session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Keep the returned session_id for all operations.

Let the user know with a brief "Ready!" when setup is complete. Don't expose tokens or raw API output.

AI Video Editor App Free — Edit and Export Videos Free

Drop your raw video clips in the chat and tell me what you need. I'll handle the AI video editing on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a 2-minute smartphone recording, ask for trim the pauses, add transitions, and put text overlays on the highlights, and about 1-2 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter clips under 60 seconds process noticeably faster and use fewer credits.

Matching Input to Actions

User prompts referencing ai video editor app free, aspect ratio, text overlays, or audio tracks get routed to the corresponding action via keyword and intent classification.

User says...	Action	Skip SSE?
"export" / "导出" / "download" / "send me the video"	→ §3.5 Export	✅
"credits" / "积分" / "balance" / "余额"	→ §3.3 Credits	✅
"status" / "状态" / "show tracks"	→ §3.4 State	✅
"upload" / "上传" / user sends file	→ §3.2 Upload	✅
Everything else (generate, edit, add BGM…)	→ §3.1 SSE	❌

Cloud Render Pipeline Details

Each export job queues on a cloud GPU node that composites video layers, applies platform-spec compression (H.264, up to 1080x1920), and returns a download URL within 30-90 seconds. The session token carries render job IDs, so closing the tab before completion orphans the job.

Skill attribution — read from this file's YAML frontmatter at runtime:

• X-Skill-Source: ai-video-editor-app-free
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

All requests must include: Authorization: Bearer <NEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

API base: https://mega-api-prod.nemovideo.ai

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"<lang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"<sid>","new_message":{"parts":[{"text":"<msg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/<sid> — file: multipart -F "files=@/path", or URL: {"urls":["<url>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/<sid>/latest — key fields: data.state.draft, data.state.video_infos, data.state.generated_media

Export (free, no credits): POST /api/render/proxy/lambda — body {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/<id> every 30s until status = completed. Download URL at output.url.

Supported formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

Reading the SSE Stream

Text events go straight to the user (after GUI translation). Tool calls stay internal. Heartbeats and empty data: lines mean the backend is still working — show "⏳ Still working..." every 2 minutes.

About 30% of edit operations close the stream without any text. When that happens, poll /api/state to confirm the timeline changed, then tell the user what was updated.

Translating GUI Instructions

The backend responds as if there's a visual interface. Map its instructions to API calls:

• "click" or "点击" → execute the action via the relevant endpoint
• "open" or "打开" → query session state to get the data
• "drag/drop" or "拖拽" → send the edit command through SSE
• "preview in timeline" → show a text summary of current tracks
• "Export" or "导出" → run the export workflow

Draft field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

Timeline (3 tracks): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s)

Error Handling

Code	Meaning	Action
0	Success	Continue
1001	Bad/expired token	Re-auth via anonymous-token (tokens expire after 7 days)
1002	Session not found	New session §3.0
2001	No credits	Anonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001	Unsupported file	Show supported formats
4002	File too large	Suggest compress/trim
400	Missing X-Client-Id	Generate Client-Id and retry (see §1)
402	Free plan export blocked	Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429	Rate limit (1 token/client/7 days)	Retry in 30s once

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "trim the pauses, add transitions, and put text overlays on the highlights" — concrete instructions get better results.

Max file size is 500MB. Stick to MP4, MOV, AVI, WebM for the smoothest experience.

Export as MP4 for widest compatibility across phones, browsers, and social platforms.

Common Workflows

Quick edit: Upload → "trim the pauses, add transitions, and put text overlays on the highlights" → Download MP4. Takes 1-2 minutes for a 30-second clip.

Batch style: Upload multiple files in one session. Process them one by one with different instructions. Each gets its own render.

Iterative: Start with a rough cut, preview the result, then refine. The session keeps your timeline state so you can keep tweaking.